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李言俊 编 / 国防工业出版社 / 2011-02 / 平装
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系统辨识理论及应用:英文
《系统辨识理论及应用英文》主要阐述系统辨识的基本原理以及应用。《系统辨识理论及应用英文》共分14章。第1章至第4章为绪论、系统辨识常用输入信号、线性系统的经典辨识方法和动态系统的典范表达式,主要回顾和介绍了与系统的辨识有关的一些基础知识。第5章至第12章为最小二乘法辨识、极大似然法辨识、时变参数辨识方法、多输入—多输出系统的辨识、其他一些辨识方法、随机时序列模型的建立、系统结构辨识和闭环系统辨识等,介绍了系统辨识常用基本方法,是系统辨识的主要内容。第13章和第14章分别介绍了系统辨识在飞行器参数辨识中的应用和神经网络在系统辨识中的应用。
Chapter1introduction1.1ClassificationofMathematicModelsofSystemandModellingMethods1.1.1SignificationofModel1.1.2RepresentationFormsofModels1.1.3ClassificationofMathematicModels1.1.4BasicMethodstoEstablishMathematicModel1.1.5BasicPrinciplesFollowedforModeling1.2Definition,ContentandProcedureofIdentification1.2.1DefinitionofIdentification1.2.2ContentandProcedureofIdentification1.3ErrorCriteriaUsuallyUsedinIdentification1.3.1OutputErrorCriterion1.3.2InputErrorCriterion1.3.3GeneralizedErrorCriterion1.4ClassificationofSystemIdentification1.4.1Off-lineIdentification1.4.2On-lineIdentificationProblemsChapter2CommonlyUsedInputSignalsforSystemIdentification.2.1SelectiveCriteriaofInputSignalforSystemIdentification2.2WhitenoisesandItsGeneratingMethods2.2.1WhiteNoiseProcess2.2.2WhiteNoiseSequence2.2.3GeneratingMethodsofWhiteNoiseSequence2.3GenerationofPseudorandomBinarySequence-M-SequenceandItsProperties2.3.1PseudorandomNoise2.3.2GeneratingMethodofM-Sequence2.3.3PropertiesofM-Sequence2.3.4AutocorrelationFunctionofTwo-LevelM-Sequence2.3.5PowerSpectralDensityofTwo-LevelM-SequenceProblemsChapter3ClassicalIdentificationMethodsofLinearSystem3.1IdentifyImpulseResponseofLinearSystembyUseofM-Sequence3.2ObtainTransferFunctionfromImpulseFunction3.2.1TransferFunctionG(s)ofContinuousSystem3.2.2TransferFunctionofDiscreteSystem—ImpulseTransferFunctionG(z-1)ProblemsChapter4CanonicalExpressionofDynamicSystems4.1ParsimonyPrinciple4.2RepresentationsofDifferenceEquationandStateEquationofLinearSystem4.2.1RepresentationofDifferenceEquationofLinearTime-InvariantSystem4.2.2RepresentationofStateEquationofLinearSystem4.3DeterministicCanonicalStateEquations4.3.1ControllableFormofCanonicalStateEquationI4.3.2ControllableFormofCanonicalStateEquationII4.3.3ObservableFormofCanonicalStateEquationI4.3.4ObservableFormofCanonicalStateEquationII4.3.5ObservableFormofCanonicalStateEquationIofMimoSystem4.3.6ObservableFormofCanonicalStateEquationIIofMimoSystem4.4DeterministicCanonicalDifferenceEquations4.5StochasticCanonicalStateEquations4.6StochasticCanonicalDifferenceEquations4.7PredictionErrorEquationProblemsChapter5Least-SquaresIdentification5.1LeastSquareMethod5.1.1AlgorithnsofLeast-SquareEstimation5.1.2InputSignalsforLeast-SquaresEstimation5.1.3ProbabilityPropertiesofLeast-SquaresEstimation5.2AKindofLeastSquresWhichNeedNotInvertMatrix5.3RecursiveLeastSquares5.4AuxiliaryVariableMethod5.5RecursiveAuxiliaryVariableMethod5.6GeneralizedLeastSquares5.7AnAlternativeGeneralizedLeastSquaresTechnique(HsiaMethod)5.8ExtendedMatrixMethod5.9MultistageLeastSquares5.9.1TheFirstAlgorithm5.9.2TheSecondAlgorithm5.9.3TheThirdAlgorithm5.10FastMultistageLeastSquaresProblemsChapter6Maximum-LikelihoodIdentificationChapter8IdentificationofMulti-InputMulti-OutputSystemsChapter9SomeOtherKindsofIdentificationMethodsChapter10EstablishmentofRandomTimeSeriesModelsChapter11StructureIdentificationofSystemChapter12IdentificationofClosed-LoopSystemChapter13ApplicationofSystemIdentificationtoParameterIdentificationofAircraftChapter14ApplicatiomofNeuralNetworktoSystemIdentificationReferences
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